{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3f4a1faf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_excel('数据源.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9cacd591",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>销售日期</th>\n",
       "      <th>员工工号</th>\n",
       "      <th>销售员</th>\n",
       "      <th>货号</th>\n",
       "      <th>销售单编号</th>\n",
       "      <th>销量</th>\n",
       "      <th>销售额</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>SS0141</td>\n",
       "      <td>钱多多</td>\n",
       "      <td>STY0026</td>\n",
       "      <td>C001S001-2017-01-01-001</td>\n",
       "      <td>1</td>\n",
       "      <td>561.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>SS0039</td>\n",
       "      <td>金花花</td>\n",
       "      <td>STY0047</td>\n",
       "      <td>C001S001-2017-01-01-002</td>\n",
       "      <td>1</td>\n",
       "      <td>270.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>SS0035</td>\n",
       "      <td>胡大花</td>\n",
       "      <td>STY0002</td>\n",
       "      <td>C001S001-2017-01-01-003</td>\n",
       "      <td>1</td>\n",
       "      <td>322.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>SS0035</td>\n",
       "      <td>胡大花</td>\n",
       "      <td>STY0016</td>\n",
       "      <td>C001S001-2017-01-01-003</td>\n",
       "      <td>1</td>\n",
       "      <td>222.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>SS0035</td>\n",
       "      <td>胡大花</td>\n",
       "      <td>STY0041</td>\n",
       "      <td>C001S001-2017-01-01-003</td>\n",
       "      <td>1</td>\n",
       "      <td>608.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24081</th>\n",
       "      <td>2018-07-31</td>\n",
       "      <td>SS0122</td>\n",
       "      <td>赵里</td>\n",
       "      <td>STY1390</td>\n",
       "      <td>C001S001-2018-07-31-022</td>\n",
       "      <td>1</td>\n",
       "      <td>276.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24082</th>\n",
       "      <td>2018-07-31</td>\n",
       "      <td>SS0041</td>\n",
       "      <td>王浩然</td>\n",
       "      <td>STY1040</td>\n",
       "      <td>C001S001-2018-07-31-023</td>\n",
       "      <td>1</td>\n",
       "      <td>137.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24083</th>\n",
       "      <td>2018-07-31</td>\n",
       "      <td>SS0041</td>\n",
       "      <td>王浩然</td>\n",
       "      <td>STY1051</td>\n",
       "      <td>C001S001-2018-07-31-023</td>\n",
       "      <td>1</td>\n",
       "      <td>137.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24084</th>\n",
       "      <td>2018-07-31</td>\n",
       "      <td>SS0041</td>\n",
       "      <td>王浩然</td>\n",
       "      <td>STY1276</td>\n",
       "      <td>C001S001-2018-07-31-024</td>\n",
       "      <td>1</td>\n",
       "      <td>207.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24085</th>\n",
       "      <td>2018-07-31</td>\n",
       "      <td>SS0041</td>\n",
       "      <td>王浩然</td>\n",
       "      <td>STY1383</td>\n",
       "      <td>C001S001-2018-07-31-024</td>\n",
       "      <td>1</td>\n",
       "      <td>227.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24086 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            销售日期    员工工号  销售员       货号                    销售单编号  销量    销售额\n",
       "0     2017-01-01  SS0141  钱多多  STY0026  C001S001-2017-01-01-001   1  561.2\n",
       "1     2017-01-01  SS0039  金花花  STY0047  C001S001-2017-01-01-002   1  270.1\n",
       "2     2017-01-01  SS0035  胡大花  STY0002  C001S001-2017-01-01-003   1  322.6\n",
       "3     2017-01-01  SS0035  胡大花  STY0016  C001S001-2017-01-01-003   1  222.7\n",
       "4     2017-01-01  SS0035  胡大花  STY0041  C001S001-2017-01-01-003   1  608.5\n",
       "...          ...     ...  ...      ...                      ...  ..    ...\n",
       "24081 2018-07-31  SS0122   赵里  STY1390  C001S001-2018-07-31-022   1  276.2\n",
       "24082 2018-07-31  SS0041  王浩然  STY1040  C001S001-2018-07-31-023   1  137.8\n",
       "24083 2018-07-31  SS0041  王浩然  STY1051  C001S001-2018-07-31-023   1  137.8\n",
       "24084 2018-07-31  SS0041  王浩然  STY1276  C001S001-2018-07-31-024   1  207.0\n",
       "24085 2018-07-31  SS0041  王浩然  STY1383  C001S001-2018-07-31-024   1  227.8\n",
       "\n",
       "[24086 rows x 7 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "54b8f853-b48a-4806-819e-763250adeaa7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<pandas.io.formats.style.Styler object at 0x000001FCC18D1820>\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# 读取Excel文件\n",
    "data = pd.read_excel('数据源.xlsx')\n",
    "\n",
    "# 定义样式函数\n",
    "def highlight_sales(s):\n",
    "    return ['background-color: yellow' if v > 2 else '' for v in s['销量']]\n",
    "\n",
    "# 应用样式函数\n",
    "styled_df = data.style.apply(highlight_sales, axis=1)\n",
    "\n",
    "# 打印样式化的DataFrame\n",
    "print(styled_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "646280db-4bd5-4c65-b2dd-1403ce2f5bac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      年份  月份    销售业绩  去年同期销售业绩\n",
      "0   2017   1  403714       NaN\n",
      "1   2017   2  250851       NaN\n",
      "2   2017   3  328411       NaN\n",
      "3   2017   4  456248       NaN\n",
      "4   2017   5  450593       NaN\n",
      "5   2017   6  288193       NaN\n",
      "6   2017   7  431125       NaN\n",
      "7   2017   8  389990       NaN\n",
      "8   2017   9  344284       NaN\n",
      "9   2017  10  476489       NaN\n",
      "10  2017  11  408415       NaN\n",
      "11  2017  12  419192       NaN\n",
      "12  2018   1  287180  403714.0\n",
      "13  2018   2  492663  250851.0\n",
      "14  2018   3  452362  328411.0\n",
      "15  2018   4  475441  456248.0\n",
      "16  2018   5  463320  450593.0\n",
      "17  2018   6  410237  288193.0\n",
      "18  2018   7  405584  431125.0\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# 创建数据字典\n",
    "data = {\n",
    "    '年份': [2017] * 12 + [2018] * 7,\n",
    "    '月份': list(range(1, 13)) + list(range(1, 8)),\n",
    "    '销售业绩': [403714, 250851, 328411, 456248, 450593, 288193, 431125, 389990, 344284, 476489, 408415, 419192,\n",
    "                 287180, 492663, 452362, 475441, 463320, 410237, 405584],\n",
    "    '去年同期销售业绩': [None] * 12 + [403714, 250851, 328411, 456248, 450593, 288193, 431125]\n",
    "}\n",
    "\n",
    "# 创建DataFrame\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# 设置显示选项\n",
    "pd.set_option('display.max_columns', None)  # 显示所有列\n",
    "pd.set_option('display.width', 1000)        # 设置显示宽度\n",
    "\n",
    "# 打印表格\n",
    "print(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a03aac1f-327b-4099-a8b1-a1af415727bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           销售业绩      上月业绩\n",
      "月份 年                     \n",
      "1  2017  403714       NaN\n",
      "2  2017  250851  403714.0\n",
      "3  2017  328411  250851.0\n",
      "4  2017  456248  328411.0\n",
      "5  2017  450593  456248.0\n",
      "6  2017  288193  450593.0\n",
      "7  2017  431125  288193.0\n",
      "8  2017  389990  431125.0\n",
      "9  2017  344284  389990.0\n",
      "10 2017  476489  344284.0\n",
      "11 2017  408415  476489.0\n",
      "12 2017  419192  408415.0\n",
      "1  2018  287180  419192.0\n",
      "2  2018  492663  287180.0\n",
      "3  2018  452362  492663.0\n",
      "4  2018  475441  452362.0\n",
      "5  2018  463320  475441.0\n",
      "6  2018  410237  463320.0\n",
      "7  2018  405584  410237.0\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# 创建数据字典\n",
    "data = {\n",
    "    '年': [2017]*12 + [2018]*7,\n",
    "    '月份': list(range(1, 13)) + list(range(1, 8)),\n",
    "    '销售业绩': [403714, 250851, 328411, 456248, 450593, 288193, 431125, 389990, 344284, 476489, 408415, 419192,\n",
    "                 287180, 492663, 452362, 475441, 463320, 410237, 405584],\n",
    "    '上月业绩': [None, 403714, 250851, 328411, 456248, 450593, 288193, 431125, 389990, 344284, 476489, 408415, \n",
    "                 419192, 287180, 492663, 452362, 475441, 463320, 410237]\n",
    "}\n",
    "\n",
    "# 创建DataFrame\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# 将索引设置为'年'和'月份'的组合\n",
    "df.set_index(['年', '月份'], inplace=True)\n",
    "\n",
    "# 重排索引以匹配图片中的顺序\n",
    "df = df.reorder_levels([1, 0])\n",
    "\n",
    "# 按照图片中的格式显示DataFrame\n",
    "pd.set_option('display.width', 1000)\n",
    "print(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b5926531-31c0-4b8d-8b8b-8d80a096a0b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      年份  月份    销售业绩  去年同期销售业绩\n",
      "0   2017   1  403714    403714\n",
      "1   2017   2  250851    654565\n",
      "2   2017   3  328411    982976\n",
      "3   2017   4  456248   1439223\n",
      "4   2017   5  450593  18899817\n",
      "5   2017   6  288193   2178009\n",
      "6   2017   7  431125   2609134\n",
      "7   2017   8  389990   2999124\n",
      "8   2017   9  344284   3343408\n",
      "9   2017  10  476489   3819897\n",
      "10  2017  11  408415   4228312\n",
      "11  2017  12  419192   4647504\n",
      "12  2018   1  287180    287180\n",
      "13  2018   2  492663    779843\n",
      "14  2018   3  452362   1232205\n",
      "15  2018   4  475441   1707647\n",
      "16  2018   5  463320   2170967\n",
      "17  2018   6  410237   2581204\n",
      "18  2018   7  405584   2986787\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# 假设的表格数据\n",
    "data = {\n",
    "    '年份': [2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017,\n",
    "             2018, 2018, 2018, 2018, 2018, 2018, 2018],\n",
    "    '月份': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
    "             1, 2, 3, 4, 5, 6, 7],\n",
    "    '销售业绩': [403714, 250851, 328411, 456248, 450593, 288193, 431125, 389990, 344284, 476489, 408415, 419192,\n",
    "                 287180, 492663, 452362, 475441, 463320, 410237, 405584],\n",
    "    '去年同期销售业绩': [403714, 654565, 982976, 1439223, 18899817, 2178009, 2609134, \n",
    "        2999124, 3343408, 3819897, 4228312, 4647504, 287180, 779843, \n",
    "        1232205, 1707647, 2170967, 2581204, 2986787]\n",
    "}\n",
    "\n",
    "# 创建DataFrame\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# 设置显示所有列\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "# 打印表格\n",
    "print(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0a5ecd66-dad3-4b49-a927-7756e54f67a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       销售日期    员工工号  销售员       货号                    销售单编号  销量    销售额    会员ID\n",
      "0  2017/1/1  SS0141  钱多多  STY0026  C0015001-2017-01-01-001   1  561.2  M11874\n",
      "1  2017/1/1  SS0039  金花花  STY0047  C0015001-2017-01-01-002   1  270.1  M15837\n",
      "2  2017/1/1  SS0035  胡大花  STY0002  C0015001-2017-01-01-003   1  322.6  M10256\n",
      "3  2017/1/1  SS0035  胡大花  STY0016  C0015001-2017-01-01-003   1  222.7  M10256\n",
      "4  2017/1/1  SS0035  胡大花  STY0041  C0015001-2017-01-01-003   1  608.5  M10256\n",
      "5  2017/1/1  SS0079   于非  STY0001  C0015001-2017-01-01-004   1  947.0  M11408\n",
      "6  2017/1/1  SS0141  钱多多  STY0024  C0015001-2017-01-01-005   1  290.4  M19980\n",
      "7  2017/1/1  SS0141  钱多多  STY0027  C0015001-2017-01-01-005   1  270.1  M19980\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# 创建DataFrame\n",
    "data = {\n",
    "    '销售日期': ['2017/1/1', '2017/1/1', '2017/1/1', '2017/1/1', '2017/1/1',\n",
    "                '2017/1/1', '2017/1/1', '2017/1/1'],\n",
    "    '员工工号': ['SS0141', 'SS0039', 'SS0035', 'SS0035', 'SS0035', 'SS0079', 'SS0141', 'SS0141'],\n",
    "    '销售员': ['钱多多', '金花花', '胡大花', '胡大花', '胡大花', '于非', '钱多多', '钱多多'],\n",
    "    '货号': ['STY0026', 'STY0047', 'STY0002', 'STY0016', 'STY0041', 'STY0001', 'STY0024', 'STY0027'],\n",
    "    '销售单编号': ['C0015001-2017-01-01-001', 'C0015001-2017-01-01-002', 'C0015001-2017-01-01-003',\n",
    "                  'C0015001-2017-01-01-003', 'C0015001-2017-01-01-003', 'C0015001-2017-01-01-004',\n",
    "                  'C0015001-2017-01-01-005', 'C0015001-2017-01-01-005'],\n",
    "    '销量': [1, 1, 1, 1, 1, 1, 1, 1],\n",
    "    '销售额': [561.2, 270.1, 322.6, 222.7, 608.5, 947, 290.4, 270.1],\n",
    "    '会员ID': ['M11874', 'M15837', 'M10256', 'M10256', 'M10256', 'M11408', 'M19980', 'M19980']\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# 显示表格\n",
    "print(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "714d5db0-4828-4f61-ad93-0b411a17d338",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Member ID  R (Recency)  F (Frequency)  M (Monetary)\n",
      "0     M10002          121              1           207\n",
      "1     M10003          353              1           256\n",
      "2     M10004          450              1           186\n",
      "3     M10005            8              2           300\n",
      "4     M10006          461              1          1058\n",
      "5     M10007          356              2          1007\n",
      "6     M10008           24              1            55\n",
      "7     M10009          475              1            69\n",
      "8     M10011           10              3           276\n",
      "9     M10012           96              4           457\n",
      "10    M10013           90              3           432\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# 创建数据字典\n",
    "data = {\n",
    "    'Member ID': ['M10002', 'M10003', 'M10004', 'M10005', 'M10006', 'M10007', 'M10008', 'M10009', 'M10011', 'M10012', 'M10013'],\n",
    "    'R (Recency)': [121, 353, 450, 8, 461, 356, 24, 475, 10, 96, 90],\n",
    "    'F (Frequency)': [1, 1, 1, 2, 1, 2, 1, 1, 3, 4, 3],\n",
    "    'M (Monetary)': [207, 256, 186, 300, 1058, 1007, 55, 69, 276, 457, 432]\n",
    "}\n",
    "\n",
    "# 创建DataFrame\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# 打印表格\n",
    "print(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "46c80c20-f1ed-4ec7-94ae-759e68cc852a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   MemberID    R  F    M  R得分  F得分  M得分  RFM  会员分组\n",
      "0    M13649  312  1  553    1    1  111    1  流失会员\n",
      "1    M13177  270  1  553    1    1  111    1  流失会员\n",
      "2    M11548   66  1  553    2    1  211    1   新会员\n",
      "3    M13716  122  1  553    2    1  211    1   新会员\n",
      "4    M14287   51  1  553    2    1  211    1   新会员\n",
      "5    M15181  265  1  553    1    1  111    1  流失会员\n",
      "6    M14162  289  1  553    1    1  111    1  流失会员\n",
      "7    M16648  243  1  553    1    1  111    1  流失会员\n",
      "8    M16682  223  1  553    1    1  111    1  流失会员\n",
      "9    M11525  112  1  553    2    1  211    1   新会员\n",
      "10   M18568   84  1  553    2    1  211    1   新会员\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# 创建一个DataFrame\n",
    "data = {\n",
    "    \"MemberID\": [\"M13649\", \"M13177\", \"M11548\", \"M13716\", \"M14287\", \"M15181\", \"M14162\", \"M16648\", \"M16682\", \"M11525\", \"M18568\"],\n",
    "    \"R\": [312, 270, 66, 122, 51, 265, 289, 243, 223, 112, 84],\n",
    "    \"F\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
    "    \"M\": [553, 553, 553, 553, 553, 553, 553, 553, 553, 553, 553],\n",
    "    \"R得分\": [1, 1, 2, 2, 2, 1, 1, 1, 1, 2, 2],\n",
    "    \"F得分\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
    "    \"M得分\": [111, 111, 211, 211, 211, 111, 111, 111, 111, 211, 211],\n",
    "    \"RFM\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
    "    \"会员分组\": [\"流失会员\", \"流失会员\", \"新会员\", \"新会员\", \"新会员\", \"流失会员\", \"流失会员\", \"流失会员\", \"流失会员\", \"新会员\", \"新会员\"]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# 打印DataFrame\n",
    "print(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d7fc84b7-6998-4bc7-956c-f962876e37bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt  \n",
    "plt.rcParams['font.sans-serif'] = [u'SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "41720559-568b-4196-befb-0453143551e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 示例数据\n",
    "data = {\n",
    "    \"销售日期\": [\"2018/3/1\", \"2018/3/1\", \"2018/3/1\", \"2018/3/1\", \"2018/3/1\", \"2018/3/1\", \"2018/3/1\", \"2018/3/1\", \"2018/3/1\"],\n",
    "    \"员工工号\": [\"SS0122\", \"SS0035\", \"SS0035\", \"SS0035\", \"SS0035\", \"SS0122\", \"SS0041\", \"SS0035\", \"SS0035\"],\n",
    "    \"销售员\": [\"赵里\", \"胡大花\", \"胡大花\", \"胡大花\", \"胡大花\", \"赵里\", \"王浩然\", \"胡大花\", \"胡大花\"],\n",
    "    \"货号\": [\"STY1217\", \"STY1193\", \"STY0133\", \"STY0141\", \"STY1188\", \"STY1216\", \"STY1256\", \"STY1075\", \"STY1188\"],\n",
    "    \"销售单编号\": [\"C001S001-2018-03-01-002\", \"C001S001-2018-03-01-007\", \"C001S001-2018-03-01-007\", \"C001S001-2018-03-01-007\", \"C001S001-2018-03-01-009\", \"C001S001-2018-03-01-001\", \"C001S001-2018-03-01-004\", \"C001S001-2018-03-01-008\", \"C001S001-2018-03-01-008\"],\n",
    "    \"销量\": [1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
    "    \"销售额\": [414.7, 176.2, 22.3, 60.8, 553.2, 760.8, 345.5, 276.2, 553.2]\n",
    "}\n",
    "\n",
    "# 创建DataFrame\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# 创建一个matplotlib图形\n",
    "fig, ax = plt.subplots(figsize=(20, len(df)/2))  # 调整figsize以适应表格的大小\n",
    "\n",
    "# 隐藏坐标轴\n",
    "ax.axis('tight')\n",
    "ax.axis('off')\n",
    "\n",
    "# 创建表格\n",
    "table = ax.table(cellText=df.values, colLabels=df.columns, loc='center')\n",
    "\n",
    "# 调整表格字体大小\n",
    "table.auto_set_font_size(False)\n",
    "table.set_fontsize(12)\n",
    "\n",
    "# 显示图形\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1d48b410-1c8e-4115-a236-964b5a9fdbce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        货号      年份 季节       上市日期  类别  性别    系列 产品名称   零售价\n",
      "0  STY0001  2017 春  春   2017/1/1  鞋子  男子  运动系列  运动鞋  1158\n",
      "1  STY0002  2016 冬  冬  2016/10/1  服装  男子  休闲系列   夹克   578\n",
      "2  STY0003  2016 冬  冬  2016/11/1  服装  男子  休闲系列   夹克   668\n",
      "3  STY0004  2016 冬  冬  2016/11/1  服装  男子  休闲系列   卫衣   498\n",
      "4  STY0005  2016 冬  冬   2016/9/1  服装  男子  休闲系列  牛仔裤   498\n",
      "5  STY0006  2017 春  春   2017/1/1  配饰  男子  配饰系列    包   338\n",
      "6  STY0007  2017 春  春   2017/1/1  鞋子  女子  运动系列  运动鞋  1238\n",
      "7  STY0008  2017 冬  冬  2017/10/1  服装  女子  休闲系列  牛仔裤   308\n",
      "8  STY0009  2016 冬  冬  2016/10/1  鞋子  女子  运动系列  运动鞋   688\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# 创建DataFrame\n",
    "data = {\n",
    "    \"货号\": [\"STY0001\", \"STY0002\", \"STY0003\", \"STY0004\", \"STY0005\", \"STY0006\", \"STY0007\", \"STY0008\", \"STY0009\"],\n",
    "    \"年份\": [\"2017 春\", \"2016 冬\", \"2016 冬\", \"2016 冬\", \"2016 冬\", \"2017 春\", \"2017 春\", \"2017 冬\", \"2016 冬\"],\n",
    "    \"季节\": [\"春\", \"冬\", \"冬\", \"冬\", \"冬\", \"春\", \"春\", \"冬\", \"冬\"],\n",
    "    \"上市日期\": [\"2017/1/1\", \"2016/10/1\", \"2016/11/1\", \"2016/11/1\", \"2016/9/1\", \"2017/1/1\", \"2017/1/1\", \"2017/10/1\", \"2016/10/1\"],\n",
    "    \"类别\": [\"鞋子\", \"服装\", \"服装\", \"服装\", \"服装\", \"配饰\", \"鞋子\", \"服装\", \"鞋子\"],\n",
    "    \"性别\": [\"男子\", \"男子\", \"男子\", \"男子\", \"男子\", \"男子\", \"女子\", \"女子\", \"女子\"],\n",
    "    \"系列\": [\"运动系列\", \"休闲系列\", \"休闲系列\", \"休闲系列\", \"休闲系列\", \"配饰系列\", \"运动系列\", \"休闲系列\", \"运动系列\"],\n",
    "    \"产品名称\": [\"运动鞋\", \"夹克\", \"夹克\", \"卫衣\", \"牛仔裤\", \"包\", \"运动鞋\", \"牛仔裤\", \"运动鞋\"],\n",
    "    \"零售价\": [1158, 578, 668, 498, 498, 338, 1238, 308, 688]\n",
    "}\n",
    "\n",
    "# 创建DataFrame\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# 打印DataFrame\n",
    "print(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5bdd3eb5-c02b-4a17-bc1d-bf0917645619",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          日期          时间点  客流量\n",
      "0   2018/3/1  00:00-01:00    0\n",
      "1   2018/3/1  01:00-02:00    0\n",
      "2   2018/3/1  02:00-03:00    0\n",
      "3   2018/3/1  03:00-04:00    0\n",
      "4   2018/3/1  04:00-05:00    0\n",
      "5   2018/3/1  05:00-06:00    0\n",
      "6   2018/3/1  06:00-07:00    0\n",
      "7   2018/3/1  07:00-08:00    0\n",
      "8   2018/3/1  08:00-09:00    1\n",
      "9   2018/3/1  09:00-10:00    8\n",
      "10  2018/3/1  10:00-11:00    9\n",
      "11  2018/3/1  11:00-12:00   12\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# 示例数据，您可以根据实际情况替换这些数据\n",
    "data = {\n",
    "    '日期': ['2018/3/1', '2018/3/1', '2018/3/1', '2018/3/1', '2018/3/1', '2018/3/1', '2018/3/1', '2018/3/1', '2018/3/1', '2018/3/1', '2018/3/1', '2018/3/1'],\n",
    "    '时间点': [\n",
    "        '00:00-01:00', '01:00-02:00', '02:00-03:00', '03:00-04:00',\n",
    "        '04:00-05:00', '05:00-06:00', '06:00-07:00', '07:00-08:00',\n",
    "        '08:00-09:00', '09:00-10:00', '10:00-11:00', '11:00-12:00'\n",
    "    ],\n",
    "    '客流量': [0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 9, 12]\n",
    "}\n",
    "\n",
    "# 创建DataFrame\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# 打印DataFrame\n",
    "print(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f1eac8e7-0e86-4bc4-ab32-2bfaee9cc1b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         货号    年份 季节  性别      类别      上市日期  零售价  销量    销售额  库存量     货号.1    仓位\n",
      "0  STY00120  2018  春  女子      夹克  2018/1/1  378   0    0.0   40  STY0002  1号仓位\n",
      "1  STY00121  2018  春  男子      夹克  2018/1/1  498   0    0.0    2  STY0009  1号仓位\n",
      "2  STY00122  2018  春  男子    牛仔长裤  2018/1/1  278   0    0.0    0  STY0010  1号仓位\n",
      "3  STY00123  2018  春  男子    长袖衬衫  2018/1/1  108   6  587.7   37  STY0012  1号仓位\n",
      "4  STY00124  2018  春  男子    长袖衬衫  2018/1/1  188   3  222.7   20  STY0013  1号仓位\n",
      "5  STY00125  2018  春  男子    校织长裤  2018/1/1  228   0    0.0   19  STY0015  1号仓位\n",
      "6  STY00126  2018  春  女子  短袖翻领恤衫  2018/1/1  188   1  111.4   20  STY0022  1号仓位\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from io import StringIO\n",
    "\n",
    "# 模拟的数据，实际使用时请根据你的数据格式进行相应的调整\n",
    "data = \"\"\"\n",
    "货号,年份,季节,性别,类别,上市日期,零售价,销量,销售额,库存量,货号,仓位\n",
    "STY00120,2018,春,女子,夹克,2018/1/1,378,0,0,40,STY0002,1号仓位\n",
    "STY00121,2018,春,男子,夹克,2018/1/1,498,0,0,2,STY0009,1号仓位\n",
    "STY00122,2018,春,男子,牛仔长裤,2018/1/1,278,0,0,0,STY0010,1号仓位\n",
    "STY00123,2018,春,男子,长袖衬衫,2018/1/1,108,6,587.7,37,STY0012,1号仓位\n",
    "STY00124,2018,春,男子,长袖衬衫,2018/1/1,188,3,222.7,20,STY0013,1号仓位\n",
    "STY00125,2018,春,男子,校织长裤,2018/1/1,228,0,0,19,STY0015,1号仓位\n",
    "STY00126,2018,春,女子,短袖翻领恤衫,2018/1/1,188,1,111.4,20,STY0022,1号仓位\n",
    "\"\"\"\n",
    "\n",
    "# 使用StringIO来模拟文件读取\n",
    "df = pd.read_csv(StringIO(data))\n",
    "\n",
    "# 打印DataFrame\n",
    "print(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "71bf748f-d54a-47a5-851f-3d22199a6a17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     类别   销量    销售额  平均单价\n",
      "0    鞋子  200  20000  1000\n",
      "1    服装  500  20000   400\n",
      "2   TTL  700  40000   571\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = {\n",
    "    \"类别\": [\"鞋子\", \"服装\",\" TTL\"],\n",
    "    \"销量\": [200, 500, 700],\n",
    "    \"销售额\": [20000, 20000,40000],\n",
    "    \"平均单价\":[1000,400,571]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "print(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "900d0d8d-f8f1-4eb4-a0be-cc677cc56179",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     类别   销量   销售额/元  平均单价/元\n",
      "0    鞋子  200  200000    1000\n",
      "1    服装  600  250000     417\n",
      "2   TTL  800  450000     565\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = {\n",
    "    \"类别\": [\"鞋子\", \"服装\",\" TTL\"],\n",
    "    \"销量\": [200, 600, 800],\n",
    "    \"销售额/元\": [200000, 250000,450000],\n",
    "    \"平均单价/元\":[1000,417,565]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "print(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0cd1c38-d02e-42e7-ad9d-7f651f2c7937",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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